Laser & Optoelectronics Progress, Volume. 57, Issue 6, 061010(2020)
Detection of Abnormal Escalator Behavior Based on Deep Neural Network
Figures & Tables(18)
Fig. 14. Detection results of three algorithms for large targets. (a) YOLOv3; (b) Tiny YOLOv3; (c) proposed algorithm
Fig. 15. Detection results of three algorithms for small targets. (a) YOLOv3; (b) Tiny YOLOv3; (c) proposed algorithm
Table 1. 18-layer deep separable convolution structure
View tableTable 1. 18-layer deep separable convolution structure
Type/stride Filter shape Output Conv dw/1 3×3×32 208×208×32 Conv/1 1×1×32×64 208×208×64 Conv dw/2 3×3×64 104×104×64 Conv/1 1×1×64×128 104×104×128 Conv dw/1 3×3×128 104×104×128 Conv/1 1×1×128×128 104×104×128 Conv dw/2 3×3×128 52×52×128 Conv/1 1×1×128×256 52×52×256 Conv dw/1 3×3×256 52×52×256 Conv/1 1×1×256×256 52×52×256 Conv dw/2 3×3×256 26×26×256 Conv/1 1×1×256×512 26×26×512 Conv dw/1 3×3×512 26×26×512 Conv/1 1×1×512×512 26×26×512 Conv dw/2 3×3×512 13×13×512 Conv/1 1×1×512×1024 13×13×1024 Conv dw/1 3×3×1024 13×13×1024 Conv/1 1×1×1024×1024 13×13×1024
Table 2. Width and height of priori box corresponding to different k values
View tableTable 2. Width and height of priori box corresponding to different k values
k=7 k=8 k=9 k=10 k=11 k=12 (62,61) (59,62) (60,59) (55,60) (56,60) (54,59) (81,117) (83,123) (75,105) (75,106) (75,106) (73,109) (123,172) (125,78) (100,147) (100,148) (100,148) (102,70) (140,83) (131,183) (136,81) (100,152) (122,72) (102,65) (186,255) (185,280) (146,205) (146,206) (140,209) (135,222) (231,146) (206,128) (192,293) (178,112) (177,120) (151,96) (287,323) (271,210) (210,133) (192,291) (189,312) (183,183) (278,372) (278,385) (242,162) (215,216) (188,323) (280,218) (266,398) (267,415) (230,131) (302,247) (264,414) (235,246) (315,258) (287,388) (323,210)
Table 3. Analysis of abnormal target detection performance for five different algorithms
View tableTable 3. Analysis of abnormal target detection performance for five different algorithms
Detection algorithm A /% FPS /(frame·s-1) F1 /% Faster RCNN 20.20 4.52 88.05 SSD 5.00 33.33 96.72 YOLOv3 5.50 25.64 95.63 Tiny YOLOv3 26.20 50.00 83.15 Proposed algorithm 3.40 43.48 97.60
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Xunsheng Ji, Bin Teng. Detection of Abnormal Escalator Behavior Based on Deep Neural Network[J]. Laser & Optoelectronics Progress, 2020, 57(6): 061010
Paper Information
Category: Image Processing
Received: Jul. 26, 2019
Accepted: Aug. 28, 2019
Published Online: Mar. 6, 2020
The Author Email: Teng Bin (2660087950@qq.com)
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